https://github.com/dimenwarper/scimitar

Single Cell Inference of MorphIng Trajectories and their Associated Regulation (SCIMITAR)
https://github.com/dimenwarper/scimitar

Last synced: 23 days ago
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Single Cell Inference of MorphIng Trajectories and their Associated Regulation (SCIMITAR)

• Host: GitHub
• URL: https://github.com/dimenwarper/scimitar
• Owner: dimenwarper
• Created: 2016-05-13T04:02:50.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2020-06-17T18:00:55.000Z (about 4 years ago)
• Last Synced: 2024-02-24T14:34:40.900Z (4 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 14.4 MB
• Stars: 6
• Watchers: 3
• Forks: 4
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

Lists

• awesome_single_cell - SCIMITAR - [Python] - Single Cell Inference of Morphing Trajectories and their Associated Regulation module (SCIMITAR) is a method for inferring biological properties from a pseudotemporal ordering. It can also be used to obtain progression-associated genes that vary along the trajectory, and genes that change their correlation structure over the trajectory; progression co-associated genes. (Software packages / RNA-seq)
• awesome-single-cell - SCIMITAR - [Python] - Single Cell Inference of Morphing Trajectories and their Associated Regulation module (SCIMITAR) is a method for inferring biological properties from a pseudotemporal ordering. It can also be used to obtain progression-associated genes that vary along the trajectory, and genes that change their correlation structure over the trajectory; progression co-associated genes. (Software packages / Pseudotime and trajectory inference)
• awesome-single-cell - SCIMITAR - [Python] - Single Cell Inference of Morphing Trajectories and their Associated Regulation module (SCIMITAR) is a method for inferring biological properties from a pseudotemporal ordering. It can also be used to obtain progression-associated genes that vary along the trajectory, and genes that change their correlation structure over the trajectory; progression co-associated genes. (Software packages / RNA-seq)

README

        


## Single Cell Inference of MorphIng Trajectories and their Associated Regulation module

SCIMITAR provides a variety of tools to analyze trajectory maps of single-cell measurements. 

With SCIMITAR you can:

* Obtain coarse-grain, (metastable) state and transition representations of your data. This is useful when you want to get a broad sense of how your data is connected.

* Infer full-fledged Gaussian distribution trajectories from single-cell data --- not only will you get cell orderings and estiamted 'pseudotemporal' mean measurements but also pseudo-time-dependant covariance matrices so you can track how your measurements' correlation change across biological progression.

* Obtain uncertainties for a cell's psuedotemporal positioning (due to uncertainty arising from heteroscedastic noise)

* Obtain genes that significantly change throughout the progression (i.e. 'progression-associated genes')

* Obtain genes that significantly change their correlation structure throughout the progression (i.e. 'progression co-associated genes')

* Infer broad co-regulatory states and psuedotemporal dynamic gene modules from the evolving co-expression matrices.

To install SCIMITAR, follow the steps below:

1. Install the [pyroconductor](https://github.com/dimenwarper/pyroconductor) package 

2. Do the usual `python setup.py install`

3. Check out the jupyter notebooks tutorials in the tutorials directory

4. Questions, concerns, or suggestions? Thanks! Open up a ticket or pm [@dimenwarper](https://github.com/dimenwarper) (Pablo Cordero)

If you use SCIMITAR please cite the [paper](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5203771/) ;)

* Cordero and Stuart, "Tracing co-regulatory network dynamics in noisy, single-cell transcriptome trajectories", Pac. Symp. of Biocomput. (2017)

Also, take a look at the [talk slides](https://docs.google.com/presentation/d/11b7-WIlcvuJNJIUucR8_tc1BCG9D0hr102tlQIt23Oc/edit?usp=sharing).